Mining top-up transactions and online classified ads to predict urban neighborhoods socioeconomic status

Quantifying income inequalities in developing countries faces challenges regarding data publicly available. Census data, collected every five or ten years, is the only source for socioeconomic indicators. Thus, local authorities need ways of producing more frequently updated indicators. Studies conducted for developed countries (Europe and USA) use Call Detail Records (CDRs) for such a purpose. In our study we propose to exploit patterns observed in developing countries, specifically in Latin America, where mobile phone usage is pervasive even among the poorest and the dominant modality for purchasing mobile airtime is the prepaid scheme (top-ups). We analyze more than 1M top-up transactions together with more than 5K online classified ads for housing sales to predict the socioeconomic status measured at an intra-urban level for 89 neighborhoods. Using a Linear Regression with Regularization L1 (Lasso), we can explain the economic status with a prediction rate up to 71% for urban neighborhoods in Guayaquil, Ecuador. Consequently, we show evidence that top-up transactions provide effective signals to characterize urban neighborhoods socioeconomic status.